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https://github.com/hwchase17/langchain
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docs: update documentation for Vertex Embeddings Models (#25745)
- **Description:update documentation for Vertex Embeddings Models
- **Issue:NA
- **Dependencies:NA
- **Twitter handle:NA
---------
Co-authored-by: adityarane@google.com <adityarane@google.com>
This commit is contained in:
Aditya
GitHub
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{
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"cells": [
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{
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"cell_type": "markdown",
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"cell_type": "raw",
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"id": "afaf8039",
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"metadata": {},
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"source": [
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"# Google Vertex AI PaLM \n",
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"---\n",
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"sidebar_label: Google Vertex AI \n",
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"keywords: [Vertex AI, vertexai , Google Cloud, embeddings]\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9a3d6f34",
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"metadata": {},
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"source": [
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"# Google Vertex AI Embeddings \n",
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"\n",
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">[Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) is a service on Google Cloud exposing the embedding models. \n",
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"This will help you get started with Google Vertex AI Embeddings models using LangChain. For detailed documentation on `Google Vertex AI Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html).\n",
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"\n",
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"Note: This integration is separate from the Google PaLM integration.\n",
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) Customer Data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
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"| Provider | Package |\n",
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"|:--------:|:-------:|\n",
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"| [Google](https://python.langchain.com/v0.2/docs/integrations/platforms/google/) | [langchain-google-vertexai](https://python.langchain.com/v0.2/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html) |\n",
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"\n",
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"To use Vertex AI PaLM you must have the `langchain-google-vertexai` Python package installed and either:\n",
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"- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
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"- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
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"## Setup\n",
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"\n",
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"This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
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"To access Google Vertex AI Embeddings models you'll need to \n",
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"- Create a Google Cloud account \n",
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"- Install the `langchain-google-vertexai` integration package.\n",
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"\n",
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"For more information, see: \n",
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"- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
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"- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
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"\n"
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"\n",
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"\n",
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"\n",
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"### Credentials\n",
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"\n",
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"\n",
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"Head to [Google Cloud](https://cloud.google.com/free/) to sign up to create an account. Once you've done this set the GOOGLE_APPLICATION_CREDENTIALS environment variable:\n",
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"\n",
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"For more information, see:\n",
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"\n",
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"https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
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"https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth"
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]
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},
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{
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"cell_type": "markdown",
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"id": "caaba519-3476-423b-a5e4-d99a10929506",
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"metadata": {},
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"source": [
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"**OPTIONAL : Authenticate your notebook environment (Colab only)**\n",
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"\n",
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"If you're running this notebook on Google Colab, run the cell below to authenticate your environment."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"id": "b0770000-3667-439b-8c46-acc5af7c8e40",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet langchain langchain-google-vertexai"
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"import sys\n",
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"\n",
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"if \"google.colab\" in sys.modules:\n",
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" from google.colab import auth\n",
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"\n",
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" auth.authenticate_user()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9fbd4a33-2480-4ad1-8d56-aec730b3662b",
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"metadata": {},
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"source": [
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"**Set Google Cloud project information and initialize Vertex AI SDK**\n",
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"\n",
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"To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
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"\n",
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"Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"id": "36521c2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_google_vertexai import VertexAIEmbeddings"
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"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}\n",
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"LOCATION = \"us-central1\" # @param {type:\"string\"}\n",
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"\n",
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"import vertexai\n",
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"\n",
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"vertexai.init(project=PROJECT_ID, location=LOCATION)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c84fb993",
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"metadata": {},
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"source": [
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"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "39a4953b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d9664366",
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"metadata": {},
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"source": [
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"### Installation\n",
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"\n",
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"The LangChain Google Vertex AI Embeddings integration lives in the `langchain-google-vertexai` package:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "64853226",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-google-vertexai"
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]
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},
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{
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"cell_type": "markdown",
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"id": "45dd1724",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our model object and generate embeddings:\n",
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">Check the list of [Supported Models](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#supported-models)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9ea7a09b",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = VertexAIEmbeddings()"
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"from langchain_google_vertexai import VertexAIEmbeddings\n",
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"\n",
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"# Initialize the a specific Embeddings Model version\n",
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"embeddings = VertexAIEmbeddings(model_name=\"text-embedding-004\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "77d271b6",
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"metadata": {},
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"source": [
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"## Indexing and Retrieval\n",
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"\n",
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"Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\n",
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"\n",
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"Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "d817716b",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'LangChain is the framework for building context-aware reasoning applications'"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"text = \"This is a test document.\""
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"# Create a vector store with a sample text\n",
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"from langchain_core.vectorstores import InMemoryVectorStore\n",
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"\n",
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"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
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"\n",
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"vectorstore = InMemoryVectorStore.from_texts(\n",
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" [text],\n",
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" embedding=embeddings,\n",
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")\n",
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"\n",
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"# Use the vectorstore as a retriever\n",
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"retriever = vectorstore.as_retriever()\n",
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"\n",
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"# Retrieve the most similar text\n",
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"retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
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"\n",
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"# show the retrieved document's content\n",
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"retrieved_documents[0].page_content"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e02b9855",
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"metadata": {},
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"source": [
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"## Direct Usage\n",
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"\n",
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"Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
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"\n",
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"You can directly call these methods to get embeddings for your own use cases.\n",
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"\n",
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"### Embed single texts\n",
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"\n",
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"You can embed single texts or documents with `embed_query`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "0d2befcd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[-0.02831101417541504, 0.022063178941607475, -0.07454229146242142, 0.006448323838412762, 0.001955120\n"
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]
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}
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],
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"source": [
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"single_vector = embeddings.embed_query(text)\n",
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"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1b5a7d03",
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"metadata": {},
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"source": [
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"### Embed multiple texts\n",
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"\n",
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"You can embed multiple texts with `embed_documents`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "2f4d6e97",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[-0.01092718355357647, 0.01213780976831913, -0.05650627985596657, 0.006737854331731796, 0.0085973171\n",
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"[0.010135706514120102, 0.01234869472682476, -0.07284046709537506, 0.00027134662377648056, 0.01546290\n"
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]
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}
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],
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"source": [
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"query_result = embeddings.embed_query(text)"
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"text2 = (\n",
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" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
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")\n",
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"two_vectors = embeddings.embed_documents([text, text2])\n",
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"for vector in two_vectors:\n",
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" print(str(vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"cell_type": "markdown",
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"id": "98785c12",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_result = embeddings.embed_documents([text])"
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"## API Reference\n",
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"\n",
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"For detailed documentation on `Google Vertex AI Embeddings\n",
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"` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/google_vertexai/embeddings/langchain_google_vertexai.embeddings.VertexAIEmbeddings.html).\n"
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]
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}
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],
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"metadata": {
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"environment": {
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"kernel": "python310",
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"name": "tf2-gpu.2-6.m104",
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"type": "gcloud",
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"uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m104"
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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@ -97,14 +315,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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},
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"vscode": {
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"interpreter": {
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"hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
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}
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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"nbformat_minor": 5
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}
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